Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [1]:
data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Downloading mnist: 9.92MB [00:30, 328KB/s]                             
Extracting mnist: 100%|██████████| 60.0K/60.0K [00:24<00:00, 2.43KFile/s]
Downloading celeba: 1.44GB [42:10, 570KB/s]                                
Extracting celeba...

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [2]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[2]:
<matplotlib.image.AxesImage at 0x10c142908>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[3]:
<matplotlib.image.AxesImage at 0x10cce0908>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [4]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.0.0
/Users/hackintoshrao/miniconda2/envs/carnd-term1/lib/python3.5/site-packages/ipykernel/__main__.py:14: UserWarning: No GPU found. Please use a GPU to train your neural network.

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [5]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # TODO: Implement Function

    input_real = tf.placeholder(tf.float32, [None, image_width, image_height, image_channels], "input_real")
    input_z = tf.placeholder(tf.float32, [None, z_dim], "input_z")
    learning_rate = tf.placeholder(tf.float32, None, "learning_rate")

    return input_real, input_z, learning_rate


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

In [7]:
def discriminator(images, reuse=False, alpha=0.01):
    """
    Create the discriminator network
    :param images: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    # TODO: Implement Function

    leaky_relu = lambda x: tf.maximum(alpha * x, x)
    
    def conv(inputs, filters, batch_norm=True):
        outputs = tf.layers.conv2d(inputs, filters, 5, 2, 'same')
        if batch_norm:
            outputs = tf.layers.batch_normalization(outputs, training=True)
        return leaky_relu(outputs)
        
    
    with tf.variable_scope("discriminator", reuse=reuse):
        # input 28*28*3
        x1 = conv(images, 64, batch_norm=False) # 14*14*64
        x2 = conv(x1, 128) # 7*7*128
        x3 = conv(x2, 256) # 4*4*256
        
        flat = tf.reshape(x3, (-1, 4*4*256))
        logits = tf.layers.dense(flat, 1)
        out = tf.sigmoid(logits)

        return out, logits


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [8]:
def generator(z, out_channel_dim, is_train=True, alpha=0.01):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    # TODO: Implement Function
    leaky_relu = lambda x: tf.maximum(alpha * x, x)
    with tf.variable_scope("generator", reuse=not is_train):
        x1 = tf.layers.dense(z, 7*7*512)
        x1 = tf.reshape(x1, (-1, 7, 7, 512))
        x1 = tf.layers.batch_normalization(x1, training=is_train)
        x1 = leaky_relu(x1)
        # 7*7*512
        
        x2 = tf.layers.conv2d_transpose(x1, 256, 5, 1, 'SAME')
        x2 = tf.layers.batch_normalization(x2, training=is_train)
        x2 = leaky_relu(x2)
        # 7*7*256
        
        x3 = tf.layers.conv2d_transpose(x2, 128, 5, 2, 'SAME')
        x3 = tf.layers.batch_normalization(x3, training=is_train)
        x3 = leaky_relu(x3)
        # 14*14*128
    
        logits = tf.layers.conv2d_transpose(x3, out_channel_dim, 5, 2, 'SAME')
        out = tf.tanh(logits)
        # 28*28*out_channel_dim
        return out
    return None


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [11]:
def model_loss(input_real, input_z, out_channel_dim, alpha=0.9):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    # TODO: Implement Function
    
    g_model = generator(input_z, out_channel_dim)
    d_model_real, d_logits_real = discriminator(input_real)
    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)

    d_loss_real = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_logits_real) * alpha))
    d_loss_fake = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_logits_fake)))
    g_loss = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_logits_fake)))

    d_loss = d_loss_real + d_loss_fake

    return d_loss, g_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [13]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # TODO: Implement Function
    
    update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
    
    with tf.control_dependencies(update_ops):
        t_vars = tf.trainable_variables()
        
        d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
        g_vars = [var for var in t_vars if var.name.startswith('generator')]

        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)

        return d_train_opt, g_train_opt


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [14]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [17]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    # TODO: Build Model
    
    
    input_real, input_z, lr = model_inputs(data_shape[1], data_shape[2], data_shape[3], z_dim)

    d_loss, g_loss = model_loss(input_real, input_z, data_shape[3])

    d_opt, g_opt = model_opt(d_loss, g_loss, lr, beta1)
    
    
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            steps = 0
            for batch_images in get_batches(batch_size):
                steps +=1
                batch_images = batch_images * 2
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
                # Run optimizers
                _ = sess.run(d_opt, feed_dict={input_real: batch_images, input_z: batch_z, lr: learning_rate})
                _ = sess.run(g_opt, feed_dict={input_real: batch_images, input_z: batch_z, lr: learning_rate})
                
                if steps % 10 == 0:
                    train_loss_d = d_loss.eval({input_real: batch_images, input_z: batch_z})
                    train_loss_g = g_loss.eval({input_z: batch_z})

                    print("Epoch {}/{}...".format(epoch_i+1, epochs),
                          "Batch {}...".format(steps),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))

                
                    show_generator_output(sess, show_n_images, input_z, data_shape[3], data_image_mode)
                
                

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [18]:
batch_size = 128
z_dim = 128
learning_rate = 0.001
beta1 = 0.5



"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Epoch 1/2... Batch 10... Discriminator Loss: 0.9442... Generator Loss: 1.1426
Epoch 1/2... Batch 20... Discriminator Loss: 3.5503... Generator Loss: 0.0710
Epoch 1/2... Batch 30... Discriminator Loss: 0.7230... Generator Loss: 1.4455
Epoch 1/2... Batch 40... Discriminator Loss: 1.6180... Generator Loss: 0.9680
Epoch 1/2... Batch 50... Discriminator Loss: 0.7602... Generator Loss: 1.5710
Epoch 1/2... Batch 60... Discriminator Loss: 0.8398... Generator Loss: 2.5601
Epoch 1/2... Batch 70... Discriminator Loss: 1.1294... Generator Loss: 1.2683
Epoch 1/2... Batch 80... Discriminator Loss: 0.8711... Generator Loss: 2.3863
Epoch 1/2... Batch 90... Discriminator Loss: 1.0960... Generator Loss: 1.6432
Epoch 1/2... Batch 100... Discriminator Loss: 1.2118... Generator Loss: 1.7009
Epoch 1/2... Batch 110... Discriminator Loss: 1.3501... Generator Loss: 0.7134
Epoch 1/2... Batch 120... Discriminator Loss: 1.2163... Generator Loss: 1.3831
Epoch 1/2... Batch 130... Discriminator Loss: 1.3540... Generator Loss: 0.8062
Epoch 1/2... Batch 140... Discriminator Loss: 1.4361... Generator Loss: 0.5400
Epoch 1/2... Batch 150... Discriminator Loss: 1.5947... Generator Loss: 0.5083
Epoch 1/2... Batch 160... Discriminator Loss: 1.2925... Generator Loss: 1.4286
Epoch 1/2... Batch 170... Discriminator Loss: 1.5022... Generator Loss: 2.4782
Epoch 1/2... Batch 180... Discriminator Loss: 1.2301... Generator Loss: 1.4060
Epoch 1/2... Batch 190... Discriminator Loss: 1.1272... Generator Loss: 0.8068
Epoch 1/2... Batch 200... Discriminator Loss: 1.2658... Generator Loss: 0.7386
Epoch 1/2... Batch 210... Discriminator Loss: 1.1558... Generator Loss: 0.9888
Epoch 1/2... Batch 220... Discriminator Loss: 1.1341... Generator Loss: 0.8623
Epoch 1/2... Batch 230... Discriminator Loss: 1.2186... Generator Loss: 0.9207
Epoch 1/2... Batch 240... Discriminator Loss: 1.2576... Generator Loss: 0.7660
Epoch 1/2... Batch 250... Discriminator Loss: 1.2658... Generator Loss: 0.6597
Epoch 1/2... Batch 260... Discriminator Loss: 1.4751... Generator Loss: 1.9710
Epoch 1/2... Batch 270... Discriminator Loss: 1.1405... Generator Loss: 0.9808
Epoch 1/2... Batch 280... Discriminator Loss: 1.1812... Generator Loss: 0.8463
Epoch 1/2... Batch 290... Discriminator Loss: 1.4079... Generator Loss: 1.8824
Epoch 1/2... Batch 300... Discriminator Loss: 1.3511... Generator Loss: 0.5492
Epoch 1/2... Batch 310... Discriminator Loss: 1.1942... Generator Loss: 0.7590
Epoch 1/2... Batch 320... Discriminator Loss: 1.1745... Generator Loss: 0.7386
Epoch 1/2... Batch 330... Discriminator Loss: 1.1657... Generator Loss: 0.9353
Epoch 1/2... Batch 340... Discriminator Loss: 1.2163... Generator Loss: 1.1467
Epoch 1/2... Batch 350... Discriminator Loss: 2.4795... Generator Loss: 3.0427
Epoch 1/2... Batch 360... Discriminator Loss: 1.2647... Generator Loss: 0.9278
Epoch 1/2... Batch 370... Discriminator Loss: 1.1280... Generator Loss: 1.3752
Epoch 1/2... Batch 380... Discriminator Loss: 1.1105... Generator Loss: 1.2907
Epoch 1/2... Batch 390... Discriminator Loss: 1.3756... Generator Loss: 0.5467
Epoch 1/2... Batch 400... Discriminator Loss: 1.0894... Generator Loss: 1.2991
Epoch 1/2... Batch 410... Discriminator Loss: 1.0809... Generator Loss: 0.9737
Epoch 1/2... Batch 420... Discriminator Loss: 0.9971... Generator Loss: 1.1530
Epoch 1/2... Batch 430... Discriminator Loss: 1.0747... Generator Loss: 1.2683
Epoch 1/2... Batch 440... Discriminator Loss: 1.2400... Generator Loss: 0.7605
Epoch 1/2... Batch 450... Discriminator Loss: 1.1320... Generator Loss: 1.0913
Epoch 1/2... Batch 460... Discriminator Loss: 1.1861... Generator Loss: 0.8634
Epoch 2/2... Batch 10... Discriminator Loss: 1.3251... Generator Loss: 0.5975
Epoch 2/2... Batch 20... Discriminator Loss: 1.0627... Generator Loss: 1.4146
Epoch 2/2... Batch 30... Discriminator Loss: 2.0042... Generator Loss: 2.5834
Epoch 2/2... Batch 40... Discriminator Loss: 1.2527... Generator Loss: 0.8381
Epoch 2/2... Batch 50... Discriminator Loss: 1.2948... Generator Loss: 0.7861
Epoch 2/2... Batch 60... Discriminator Loss: 1.1711... Generator Loss: 0.7993
Epoch 2/2... Batch 70... Discriminator Loss: 1.3288... Generator Loss: 0.5995
Epoch 2/2... Batch 80... Discriminator Loss: 1.1243... Generator Loss: 1.3385
Epoch 2/2... Batch 90... Discriminator Loss: 1.2665... Generator Loss: 1.2203
Epoch 2/2... Batch 100... Discriminator Loss: 1.2657... Generator Loss: 1.6889
Epoch 2/2... Batch 110... Discriminator Loss: 1.6003... Generator Loss: 2.2303
Epoch 2/2... Batch 120... Discriminator Loss: 1.1733... Generator Loss: 0.8414
Epoch 2/2... Batch 130... Discriminator Loss: 1.5645... Generator Loss: 0.4249
Epoch 2/2... Batch 140... Discriminator Loss: 1.1834... Generator Loss: 0.7759
Epoch 2/2... Batch 150... Discriminator Loss: 1.1753... Generator Loss: 0.8788
Epoch 2/2... Batch 160... Discriminator Loss: 1.1913... Generator Loss: 1.4010
Epoch 2/2... Batch 170... Discriminator Loss: 1.3639... Generator Loss: 0.6469
Epoch 2/2... Batch 180... Discriminator Loss: 1.1595... Generator Loss: 1.1944
Epoch 2/2... Batch 190... Discriminator Loss: 1.4742... Generator Loss: 0.5289
Epoch 2/2... Batch 200... Discriminator Loss: 1.1788... Generator Loss: 1.4383
Epoch 2/2... Batch 210... Discriminator Loss: 1.4965... Generator Loss: 2.0205
Epoch 2/2... Batch 220... Discriminator Loss: 1.1276... Generator Loss: 0.8684
Epoch 2/2... Batch 230... Discriminator Loss: 1.6191... Generator Loss: 0.4131
Epoch 2/2... Batch 240... Discriminator Loss: 1.2124... Generator Loss: 0.8349
Epoch 2/2... Batch 250... Discriminator Loss: 1.1190... Generator Loss: 1.3690
Epoch 2/2... Batch 260... Discriminator Loss: 1.2463... Generator Loss: 1.6537
Epoch 2/2... Batch 270... Discriminator Loss: 1.1673... Generator Loss: 0.9325
Epoch 2/2... Batch 280... Discriminator Loss: 1.1752... Generator Loss: 1.0672
Epoch 2/2... Batch 290... Discriminator Loss: 1.2056... Generator Loss: 0.8392
Epoch 2/2... Batch 300... Discriminator Loss: 1.5848... Generator Loss: 0.4023
Epoch 2/2... Batch 310... Discriminator Loss: 1.2432... Generator Loss: 0.6692
Epoch 2/2... Batch 320... Discriminator Loss: 1.5368... Generator Loss: 0.4375
Epoch 2/2... Batch 330... Discriminator Loss: 1.0533... Generator Loss: 1.0902
Epoch 2/2... Batch 340... Discriminator Loss: 1.3495... Generator Loss: 0.6558
Epoch 2/2... Batch 350... Discriminator Loss: 1.5265... Generator Loss: 2.1749
Epoch 2/2... Batch 360... Discriminator Loss: 1.2392... Generator Loss: 0.7021
Epoch 2/2... Batch 370... Discriminator Loss: 1.3799... Generator Loss: 1.9442
Epoch 2/2... Batch 380... Discriminator Loss: 1.0815... Generator Loss: 0.9482
Epoch 2/2... Batch 390... Discriminator Loss: 1.2998... Generator Loss: 0.6829
Epoch 2/2... Batch 400... Discriminator Loss: 1.1130... Generator Loss: 1.0959
Epoch 2/2... Batch 410... Discriminator Loss: 1.1266... Generator Loss: 1.4416
Epoch 2/2... Batch 420... Discriminator Loss: 0.9258... Generator Loss: 1.5573
Epoch 2/2... Batch 430... Discriminator Loss: 1.2354... Generator Loss: 1.4320
Epoch 2/2... Batch 440... Discriminator Loss: 1.1381... Generator Loss: 0.7776
Epoch 2/2... Batch 450... Discriminator Loss: 1.1607... Generator Loss: 1.5595
Epoch 2/2... Batch 460... Discriminator Loss: 1.2000... Generator Loss: 0.8081

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [19]:
batch_size = 64
z_dim = 100
learning_rate = 0.0001
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch 1/1... Batch 10... Discriminator Loss: 1.6962... Generator Loss: 0.4494
Epoch 1/1... Batch 20... Discriminator Loss: 1.1936... Generator Loss: 0.7504
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Epoch 1/1... Batch 2670... Discriminator Loss: 1.2704... Generator Loss: 0.6429
Epoch 1/1... Batch 2680... Discriminator Loss: 1.0597... Generator Loss: 0.9512
Epoch 1/1... Batch 2690... Discriminator Loss: 1.1543... Generator Loss: 0.9463
Epoch 1/1... Batch 2700... Discriminator Loss: 1.1414... Generator Loss: 0.8111
Epoch 1/1... Batch 2710... Discriminator Loss: 1.0654... Generator Loss: 1.1052
Epoch 1/1... Batch 2720... Discriminator Loss: 1.5375... Generator Loss: 0.4710
Epoch 1/1... Batch 2730... Discriminator Loss: 1.3114... Generator Loss: 0.6971
Epoch 1/1... Batch 2740... Discriminator Loss: 1.0182... Generator Loss: 0.9968
Epoch 1/1... Batch 2750... Discriminator Loss: 1.0798... Generator Loss: 0.9986
Epoch 1/1... Batch 2760... Discriminator Loss: 1.2293... Generator Loss: 0.6935
Epoch 1/1... Batch 2770... Discriminator Loss: 1.4173... Generator Loss: 0.5791
Epoch 1/1... Batch 2780... Discriminator Loss: 1.4398... Generator Loss: 0.4892
Epoch 1/1... Batch 2790... Discriminator Loss: 1.1715... Generator Loss: 0.8082
Epoch 1/1... Batch 2800... Discriminator Loss: 1.0640... Generator Loss: 1.6010
Epoch 1/1... Batch 2810... Discriminator Loss: 1.4821... Generator Loss: 0.5759
Epoch 1/1... Batch 2820... Discriminator Loss: 1.0411... Generator Loss: 1.3142
Epoch 1/1... Batch 2830... Discriminator Loss: 1.3357... Generator Loss: 0.5942
Epoch 1/1... Batch 2840... Discriminator Loss: 1.2190... Generator Loss: 0.7719
Epoch 1/1... Batch 2850... Discriminator Loss: 0.9238... Generator Loss: 1.3802
Epoch 1/1... Batch 2860... Discriminator Loss: 1.1203... Generator Loss: 1.0604
Epoch 1/1... Batch 2870... Discriminator Loss: 1.1288... Generator Loss: 0.8137
Epoch 1/1... Batch 2880... Discriminator Loss: 1.1149... Generator Loss: 0.8342
Epoch 1/1... Batch 2890... Discriminator Loss: 1.0390... Generator Loss: 1.2730
Epoch 1/1... Batch 2900... Discriminator Loss: 1.1468... Generator Loss: 0.8874
Epoch 1/1... Batch 2910... Discriminator Loss: 1.1359... Generator Loss: 1.4217
Epoch 1/1... Batch 2920... Discriminator Loss: 0.9012... Generator Loss: 1.3289
Epoch 1/1... Batch 2930... Discriminator Loss: 1.0903... Generator Loss: 0.8740
Epoch 1/1... Batch 2940... Discriminator Loss: 1.1738... Generator Loss: 0.7939
Epoch 1/1... Batch 2950... Discriminator Loss: 1.0082... Generator Loss: 1.1648
Epoch 1/1... Batch 2960... Discriminator Loss: 0.8664... Generator Loss: 1.4161
Epoch 1/1... Batch 2970... Discriminator Loss: 1.1497... Generator Loss: 1.0056
Epoch 1/1... Batch 2980... Discriminator Loss: 1.0812... Generator Loss: 0.9768
Epoch 1/1... Batch 2990... Discriminator Loss: 1.0699... Generator Loss: 1.0459
Epoch 1/1... Batch 3000... Discriminator Loss: 1.2433... Generator Loss: 0.6410
Epoch 1/1... Batch 3010... Discriminator Loss: 0.9980... Generator Loss: 1.0753
Epoch 1/1... Batch 3020... Discriminator Loss: 1.2406... Generator Loss: 0.6850
Epoch 1/1... Batch 3030... Discriminator Loss: 1.2468... Generator Loss: 0.7323
Epoch 1/1... Batch 3040... Discriminator Loss: 1.5306... Generator Loss: 0.4674
Epoch 1/1... Batch 3050... Discriminator Loss: 1.0386... Generator Loss: 1.1119
Epoch 1/1... Batch 3060... Discriminator Loss: 1.4485... Generator Loss: 0.5063
Epoch 1/1... Batch 3070... Discriminator Loss: 1.5162... Generator Loss: 0.4749
Epoch 1/1... Batch 3080... Discriminator Loss: 0.9505... Generator Loss: 1.1737
Epoch 1/1... Batch 3090... Discriminator Loss: 1.1415... Generator Loss: 0.7720
Epoch 1/1... Batch 3100... Discriminator Loss: 1.1810... Generator Loss: 0.7933
Epoch 1/1... Batch 3110... Discriminator Loss: 1.0576... Generator Loss: 0.8726
Epoch 1/1... Batch 3120... Discriminator Loss: 0.9306... Generator Loss: 1.0827
Epoch 1/1... Batch 3130... Discriminator Loss: 1.0109... Generator Loss: 1.0147
Epoch 1/1... Batch 3140... Discriminator Loss: 1.0191... Generator Loss: 1.2011
Epoch 1/1... Batch 3150... Discriminator Loss: 1.0994... Generator Loss: 1.0052
Epoch 1/1... Batch 3160... Discriminator Loss: 1.5783... Generator Loss: 0.4110

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.